The model is directly integrated into [Credential Digger]((https://github.com/SAP/credential-digger) and can be used to filter the false positive password discoveries of a scan.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Training Details

Training Data

CodeBERT-base-mlm fine-tuned on a dataset for leak detection.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

More information needed

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: More information needed
Hours used: More information needed
Cloud Provider: More information needed
Compute Region: More information needed
Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

BibTeX:

TBD

Model Card Authors [optional]

SAP OSS in collaboration with Ezi Ozoani and the Hugging Face team.

Model Card Contact

More information needed

How to Get Started with the Model

The model is directly integrated into Credential Digger and can be used to filter the false positive discoveries of a scan

Click to expand

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
tokenizer = AutoTokenizer.from_pretrained("SAPOSS/password-model")
 
model = AutoModelForSequenceClassification.from_pretrained("SAPOSS/password-model")